Data science for the oil and gas industry in the Arab region

2021 ◽  
Vol 64 (4) ◽  
pp. 54-56
Author(s):  
Motaz El Saban
2020 ◽  
Author(s):  
Israel Guevara ◽  
David Ardila ◽  
Kevin Daza ◽  
Oscar Ovalle ◽  
Paola Pastor ◽  
...  

2021 ◽  
Vol 73 (02) ◽  
pp. 23-28
Author(s):  
Judy Feder

For an industry in which terms like “first ever,” “unparalleled,” “unprecedented,” and “novel” are often over-used to the point of losing their meaning, 2020 hit hard with the true meaning of those words as the COVID-19 pandemic exploded onto the world and disrupted almost everything about life as we knew it. The oil and gas industry, which had begun showing signs of recovery from a generational downturn, was hit particularly hard. Jobs were lost, companies shuttered, and supply chains upended. But the same combination of audacity and ingenuity that has driven the industry for over a century took hold quickly. Oil and gas people love - and need - to network, share ideas, transfer and apply technology, and gather intelligence. So, when in-person conferences, workshops, and tradeshows were suddenly canceled or indefinitely postponed, entities such as the Society of Petroleum Engineers scrambled to use digital technology to take those events online and make them virtual. While not a perfect replacement, virtual online events offer some unique advantages, as SPE technical directors pointed out in their annual roundup. Digitalization, long a controversial topic among many in the upstream sector, is now being called essential by the directors across the six SPE technical disciplines they lead. Automation is mentioned frequently in the directors’ comments as a growing contributor to efficiency and risk reduction. Capital discipline, balance sheet management, and cash flow are seen as crucial, as are collaboration (both internal and external) and value - ranging from core values to value-driven data, to provable, value-based outcomes. Agility and the ability to comply with environmental, social, and governance (ESG) criteria have taken on new importance. So has work - how and where we do it, and how we balance it with other aspects of life. Knowledge dissemination is considered more important than ever. Uncertainty continues to characterize the upstream sector, even more so than before the pandemic, but at least one thing is certain: The work of the industry, and the way in which people who comprise it work, is forever changed. The SPE technical disciplines and the directors who lead them are as follows. Completions - Terry Palisch, CARBO Ceramics Data Science and Engineering Analytics - Silviu Livescu, Baker Hughes Drilling - David Reid, NOV HSE and Sustainability - Annamaria Petrone, Eni Production and Facilities - Robert Pearson, Glynn Resources Reservoir - Erdal Ozkan, Colorado School of Mines Here, they reflect on a truly unprecedented year and share their outlooks for their disciplines going forward.


2021 ◽  
Author(s):  
Mason Dykstra ◽  
Ben Lasscock

Abstract In this paper we present an example of improved approaches for how to interact with data and leverage artificial intelligence for the subsurface. Currently, subsurface workflows typically rely on a lot of time-consuming manual input and analysis, but the promise of artificial intelligence is that, once properly trained, an AI can take care of the more routine tasks, leaving the domain expert free to work on more complex and creative parts of the job. Artificial intelligence work on subsurface datasets in recent years has typically taken the form of research and proof of concept type work, with a lot of one-off solutions showing up in the literature using new and innovative ideas (e.g. Hussein et al, 2021; Misra et al, 2019). Oftentimes this work requires a good degree of data science knowledge and programming skills on the part of the scientist, putting many of the approaches outlined in these and a multitude of other papers out of reach for many subsurface experts in the Oil and Gas industry. In order for Artificial Intelligence to become applied as part of regular workflows in the subsurface, the industry needs tools built to help subsurface experts access AI techniques in a more practical, targeted way. We present herein a practical guide to help in developing applied artificial Intelligence tools to roll out within your organization or to the industry more broadly.


Author(s):  
Zeeshan Tariq ◽  
Murtada Saleh Aljawad ◽  
Amjed Hasan ◽  
Mobeen Murtaza ◽  
Emad Mohammed ◽  
...  

AbstractThis study offered a detailed review of data sciences and machine learning (ML) roles in different petroleum engineering and geosciences segments such as petroleum exploration, reservoir characterization, oil well drilling, production, and well stimulation, emphasizing the newly emerging field of unconventional reservoirs. The future of data science and ML in the oil and gas industry, highlighting what is required from ML for better prediction, is also discussed. This study also provides a comprehensive comparison of different ML techniques used in the oil and gas industry. With the arrival of powerful computers, advanced ML algorithms, and extensive data generation from different industry tools, we see a bright future in developing solutions to the complex problems in the oil and gas industry that were previously beyond the grip of analytical solutions or numerical simulation. ML tools can incorporate every detail in the log data and every information connected to the target data. Despite their limitations, they are not constrained by limiting assumptions of analytical solutions or by particular data and/or power processing requirements of numerical simulators. This detailed and comprehensive study can serve as an exclusive reference for ML applications in the industry. Based on the review conducted, it was found that ML techniques offer a great potential in solving problems in almost all areas of the oil and gas industry involving prediction, classification, and clustering. With the generation of huge data in everyday oil and gas industry activates, machine learning and big data handling techniques are becoming a necessity toward a more efficient industry.


2020 ◽  
Vol 78 (7) ◽  
pp. 861-868
Author(s):  
Casper Wassink ◽  
Marc Grenier ◽  
Oliver Roy ◽  
Neil Pearson

2004 ◽  
pp. 51-69 ◽  
Author(s):  
E. Sharipova ◽  
I. Tcherkashin

Federal tax revenues from the main sectors of the Russian economy after the 1998 crisis are examined in the article. Authors present the structure of revenues from these sectors by main taxes for 1999-2003 and prospects for 2004. Emphasis is given to an increasing dependence of budget on revenues from oil and gas industries. The share of proceeds from these sectors has reached 1/3 of total federal revenues. To explain this fact world oil prices dynamics and changes in tax legislation in Russia are considered. Empirical results show strong dependence of budget revenues on oil prices. The analysis of changes in tax legislation in oil and gas industry shows that the government has managed to redistribute resource rent in favor of the state.


2011 ◽  
pp. 19-33
Author(s):  
A. Oleinik

The article deals with the issues of political and economic power as well as their constellation on the market. The theory of public choice and the theory of public contract are confronted with an approach centered on the power triad. If structured in the power triad, interactions among states representatives, businesses with structural advantages and businesses without structural advantages allow capturing administrative rents. The political power of the ruling elites coexists with economic power of certain members of the business community. The situation in the oil and gas industry, the retail trade and the road construction and operation industry in Russia illustrates key moments in the proposed analysis.


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